[New user question] Using autograd for derivatives about pygmo2 HOT 2 CLOSED

Would anyone be able to help with this?

from pygmo2.

Hey,
yes, you can pass gradients and hessian for the algorithms that make use of them. Just add the necessary methods to your udp, its that simple.

For example have a look at the code here: https://esa.github.io/pygmo2/tutorials/nlopt_basics.html

In particular at the section "I do not have the gradient".

I know its confusing as you do have the gradient, but bear with me. In that example:

    def gradient(self, x):
        return pg.estimate_gradient_h(lambda x: self.fitness(x), x)

we have added to the UDP a method called gradient. And we return a numerical estimate.

If you do have the gradient (for example using autodiff functionalities), you can just return it there!

from pygmo2.